Identifying the specific aspects of sleep that relate to incident dementia is the first step towards the development of sleep interventions to reduce dementia risk. Detailed overnight sleep studies, known as polysomnography (PSG), provide the gold-standard assessment of sleep. As obtaining PSG is burdensome, studies with PSG tend to enroll a limited number of participants and consequently have limited statistical power to detect small but potentially important associations between sleep and dementia. We propose to curate data from 5 large population-based cohorts (Atherosclerosis Risk in Communities, Cardiovascular Health Study, Framingham Heart Study, Osteoporotic Fractures in Men, and the Study of Osteoporotic Fractures) with methodologically consistent sleep studies and neurocognitive outcomes. By combining study-level data in meta-analysis, we propose the following aims: Aim 1 is to examine the aspects of sleep that relate to a higher risk of incident Alzheimer's disease (AD) dementia (N=2776, 499 incident cases). We will capture 134 sleep metrics, measuring all aspects of sleep neurophysiology. We will then identify clusters and calculate the first principal component from each cluster as the exposers. We will further assess the association between cluster specific sleep metrics and outcomes using least absolute shrinkage and selection operator (LASSO) regression 1.a. We will examine each aspect of sleep neurophysiology with respect to the risk of incident AD dementia, after accounting for known confounders. 1.b. We will leverage our statistical power to explore differences by age decades, sex, and genetic risk (e.g., APOE ?4 positivity). Aim 2 is to examine the aspects of sleep (defined in Aim 1) that relate cross-sectionally to dementia endophenotypes. As poor sleep is potentially modifiable, it is important to know whether poor sleep is related to preclinical phenotypes of dementia?a time when dementia risk may still be malleable. Brain atrophy on MRI and subtle deficits in cognitive ability precede dementia diagnosis by up to a decade. We will relate each sleep marker to general and domain-specific cognitive performance (N=6723) as well as brain volume (total brain and hippocampal) and brain injury (white matter disease, silent infarcts) on MRI (N=1157). Aim 3 is to examine whether changes in sleep neurophysiology over ~6 years predict incident dementia (N=1558, 275 events), cognition (N=3065), or brain volume (N=763). Leveraging repeated PSGs ~6 years apart, we will examine if changes in sleep neurophysiology relate to incident AD dementia, brain volume, or cognitive function. Our large analysis of community-based participants from across the U.S. will provide the most robust evidence yet on the associations between sleep and AD dementia risk. Moreover, leveraging our large pooled sample size to examine subgroup differences (e.g., by age decades, sex and APOE) and the comprehensive investigation of sleep neurophysiology, including innovative sleep measures (e.g., spindle density), may inform therapeutic strategies for dementia prevention by identifying subgroups most at risk, new biomarkers to improve dementia risk stratification, and novel biological pathways.